Abstract
In instance-based learning, two different metrics are usually used for continuous-valued attributes and nominal attributes. The problem of using different metrics in domains which have both types of attribute has been mitigated by methods such as attribute and instance weightings in instance-based learning.
This paper investigates a method that treats both types of attribute using a single uniform metric in instance-based learning. The method transforms continuous-valued attributes into nominal attributes through discretisation at the outset. We empirically examine the approach using both real-world and artificial datasets to characterise the benefits of discretisation and using a single uniform metric in instance-based learning. Results indicate that our approach can be beneficial to instance-based learning in domains which have noise or irrelevant attributes.
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© 1995 Springer-Verlag Berlin Heidelberg
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Ting, K.M. (1995). Towards using a single uniform metric in instance-based learning. In: Veloso, M., Aamodt, A. (eds) Case-Based Reasoning Research and Development. ICCBR 1995. Lecture Notes in Computer Science, vol 1010. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60598-3_52
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DOI: https://doi.org/10.1007/3-540-60598-3_52
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